Then "they're doing something amazing, they don't need permission, and the cat is already out of the bag, and similar musings".
Seriously, it's both copyright infringement, and unethical. This is why I don't use any of the popular AI tools, or even AI add-ons in Evernote, Notion, etc. They all link back to the usual suspects.
Ah, the Uber theory of law. Works surprisingly well for some reason.
I can see the problem where direct and faithful replication is possible but where it isn't is there still a problem? Or is the automatable aspect, the scale at which it can occur, that is the problem?
An AI system consumes something perfectly, then ingrains it into its weights perfectly, and becomes capable of imitating the same thing perfectly. Plus, ther are no other internal or external factors which affect these "generation" over time. Hence, it mixes and reproduces based on what it consumed, solely.
I might get inspired by people, and add my own values to it, iterate over it and diverge from what I'm inspired from ultimately to create my own style. AI doesn't work like that. Also, if I do the same amount of inspiration with the same precision and accuracy, I'll be neck deep in accusations and lawsuits (for the right reasons).
As a result, just because we fail to ask the right questions to reproduce the training data verbatim or almost verbatim doesn't mean that the information is not there. At the end, a neural network is a compression algorithm which encodes data in terms of weights. Given the correct input, you can regenerate the training data as is.
Unless you have special abilities, you can't read 500 books an hour, remember them perfectly, and generate derivative works by mashing all of them together. If I do and try to sell a novel, I'll be ridiculed no end. If I write a Ph.D. the same way and try to defend it, I'll be banned from academia for three lifetimes at least.
For more elaboration on the subject, see [0].
If you divide Stable Diffusion's file size by the number of images used to train it, you get something like 1.2 bits per image, and it is physically impossible to get this kind of a compression ratio.
The actual problem with AI is that it sometimes plagiarizes random fragments of the work it is trained on, even if it is not the user's intend, and we currently don't really know how to fully prevent this.
Same for code generating models trained on Open Source and Free Software. Tons of licenses violated, from strong copyleft to source available models and reproduced (almost) verbatim with comments intact.
Some researcher's codebase is almost completely reproducible without any licensing information just by hinting the function names.
Maybe for the image compression it's borderline impossible for now due to network size, but for text and code, generation of training data almost verbatim is very possible and straightforward.
Also in image generation models, style transfer is the bigger problem, because it completely eliminates the artist who uses/created the style in the first place. "You pioneered this, and we fine tuned this model with your images, and we can do your work for free, without you, have a nice day". However, the artist's life expenses doesn't disappear when they're transferred to an image generation model.
This is also unethical.
Just because you can doesn’t mean you should. That's what I'm trying to say.
IIRC it was like 1.4 bytes before adding in random initial and prompt. And Amiga Four-byte Burger is 4 bytes long.
[0]: https://bytecellar.com/2023/04/24/lost-amiga-four-byte-burge...
Open science repositories would take down the "dataset" immediately (or at least limit its access) if a copyright holder brings the matter to the eyes of the admins.
In the case of The Pile, "processing for language modelling" means "converting epub and pdf into plain text, maybe deduplicating, maybe removing some sorts of detectably malformed files"
So not a particularly lossy conversion.